Paper:

# Generalized Predictive PID Control for Main Steam Temperature Based on Improved PSO Algorithm

## Zhongda Tian, Shujiang Li, and Yanhong Wang

College of Information Science and Engineering, Shenyang University of Technology

Shenyang 110870, China

The large inertia and long delay characteristics of main steam temperature control system in thermal power plants will reduce the system control performance. In order to improve the system control performance, a generalized predictive PID control for main steam temperature strategy based on improved particle swarm optimization algorithm is proposed. The performance index of incremental PID controller of main control loop and PD controller of auxiliary control loop based on generalized predictive control algorithm is established. An improved particle swarm optimization algorithm with better fitness and faster convergence speed is proposed for online parameters optimization of performance index. The optimal control value of PID controller and PD controller can be obtained. The simulation experiment compared with fuzzy PID and fuzzy neural network is carried out. Simulation results show that proposed control method has faster response speed, smaller overshoot and control error, better tracking performance, and reduces the lag effect of the control system.

- [1] W. K. Hu and Y. J. Fang, “Multimodel parameters identification for main steam temperature of ultra-supercritical units using an improved genetic algorithm,” J. of Energy Engineering, Vol.139, pp. 290-298, 2013.
- [2] S. Benammar, A. Khellaf, and K. Mohammedi, “Contribution to the modeling and simulation of solar power tower plants using energy analysis,” Energy Conversion and Management, Vol.78, pp. 923-930, 2014.
- [3] H. Zhang, W. Lu, J. H. Yang, S. Q. Sheng, and H. B. Guo, “Multi-model switching predictive functional control of boiler main steam temperature,” Information Technology J., Vol.12, pp. 391-396, 2013.
- [4] G. L. Wang, W. W. Yan, S. H. Chen, X. Zhang, and H. H. Shao, “Multivariable constrained predictive control of main steam temperature in ultra-supercritical coal-fired power unit,” J. of the Energy Institute, Vol.88, pp. 181-187, 2015.
- [5] S. Q. Tian, Q. Wei, and X. F. Mu, “Simulation research on fuzzy control of main steam temperature in power series,” 2014 Int. Conf. on Electronic Engineering and Information Science, pp. 555-559, 2014.
- [6] T. Nahlovsky and O. Modrlak, “The fuzzy approach to the temperature control of superheated steam,” 2013 17th Int. Conf. on System Theory, Control and Computing, pp. 374-379, 2013.
- [7] D. Ze, M. Lei, and H. Pu, “Research on a modified smith predictive control scheme of main steam temperature of circulating fluidized bed,” Research J. of Applied Sciences, Engineering and Technology, Vol.14, pp. 2216-2221, 2012.
- [8] D. M. Xi, L. J. Hu, and Y. C. Gong, “Adaptive predictor gain control of the main steam temperature,” 2012 Int. Conf. on Software Engineering, Knowledge Engineering and Information Engineering, pp. 125-130, 2012.
- [9] H. Y. Wang, “Main steam temperature control system based on fuzzy control scheme,” 2014 6th Int. Conf. on Intelligent Human-Machine Systems and Cybernetics, pp. 93-95, 2014.
- [10] C. L. Liu and Y. B. Zhang, “Simulation of main steam temperature control system based on fuzzy PID and improved smith predictor,” 2013 2nd Int. Conf. on Energy and Environmental Protection, pp. 280-285, 2013.
- [11] F. Jiang, “Research on designs of city temperature control systems based on particle swarm algorithm and neural network,” J. of Convergence Information Technology, Vol.7, pp. 484-492, 2012.
- [12] P. F. Niu, L. Gao, F. D. Meng, G. L. Chen, and J. Zhang, “Adaptive fuzzy control based on neural network decoupling for circulating fluidized bed boiler combustion system,” Chinese J. of Scientific Instrument, Vol.32, pp. 1021-1025, 2011.
- [13] Y. M. Nie and Z. Q. He, “Optimization of the main seam temperature PID parameters based on improve BP neural network,” 2nd Int. Conf. on Simulation and Modeling Methodologies, Technologies and Applications, pp. 113-116, 2015.
- [14] S. Goto, M. Nakamura, and S. Matsumura, “Automatic realization of human experience for controlling variable-pressure boilers,” Control Engineering Practice, Vol.10, pp. 15-22, 2002.
- [15] L. Zhao and X. Guo, “The design of steam temperature control system with fuzzy neural network based on GA,” 2nd Int. Conf. on Electric Information and Control Engineering, pp. 1710-1713, 2012.
- [16] M. Sun, X. H. Wang, and P. Han, “TS fuzzy identification for main steam temperature system using improved particle swarm optimization,” 8th World Congress on Intelligent Control & Automation, pp. 5900-5905, 2010.
- [17] N. A. Mazalan, A. A. Malek, M. A. Wahid, and M. Mailah, “Review of control strategies employing neural network for main steam temperature control in thermal power plant,” J. Teknologi, Vol.66, pp. 73-76, 2014.
- [18] Q. N. Tran, L. Özkan, and A. C. P. M. Backx, “Generalized predictive control tuning by controller matching,” J. of Process Control, Vol.25, pp. 4889-4894, 2014.
- [19] Z. D. Tian, X. W. Gao, B. L. Gong, and T. Shi, “Time-delay compensation method for networked control system based on time-delay prediction and implicit PIGPC,” Int. J. of Automation and Computing, Vol.12, pp. 648-656, 2015.
- [20] J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” IEEE Int. Conf. on Neural Networks, Proc., pp. 1942-1948, 1995.
- [21] Q. F. Liu, W. H. Wei, H. Q. Yuan, Z. H. Zhan, and Y. Lin, “Topology selection for particle swarm optimization,” Information Sciences, Vol.363, pp. 154-173, 2016.
- [22] T. Kerdphol, K. Fuji, Y. Mitani, M. Watanabe, and Y. Quadih, “Optimization of a battery energy storage system using particle swarm optimization for stand-alone microgrids,” Int. J. of Electrical Power & Energy Systems, Vol.81, pp. 32-39, 2016.
- [23] Z. D. Tian, S. J. Li, Y. H. Wang, and X. D. Wang, “A multi-model fusion soft sensor modelling method and its application in rotary kiln calcination zone temperature prediction,” Trans. of the Institute of Measurement and Control, Vol.38, pp. 110-124, 2016.